Abstract
The generalization performance is the main purpose of machine learning theoretical research. This note mainly focuses on a theoretical analysis of learning machine with negatively associated dependent input sequence. The explicit bound on the rate of uniform convergence of the empirical errors to their expected error based on negatively associated dependent input sequence is obtained by the inequality of Joag-dev and Proschan. The uniform convergence approach is used to estimate the convergence rate of the sample error of learning machine that minimize empirical risk with negatively associated dependent input sequence. In the end, we compare these bounds with previous results.
Supported in part by NSFC under grant 60403011.
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Zou, B., Li, L., Xu, J. (2006). The Generalization Performance of Learning Machine with NA Dependent Sequence. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_82
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DOI: https://doi.org/10.1007/11795131_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
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